Position information is essential to locate an object. The visual grounding aligns visual position information in the scene (e.g., coordinates of objects) with the location described in natural language (e.g., “left” or “right”). Unlike 2D images with static views, 3D scenes can freely rotate to different views. These dynamic view changes make 3D visual grounding more challenging. The view changes will directly affect the position encoding, e.g., coordinates of objects, thus bringing more difficulty in representing 3D objects. For example, as shown in Fig. REF , the rightmost pillow in the front view becomes the leftmost one in the back view. This discrepancy commonly exists in real applications: when a robot or an intelligent agent is navigating in the 3D scene, its view is usually different from the speaker's or commander's view. Such view discrepancy can bring more ambiguity in identifying the location of targets. To alleviate this view issue, when Referit3D [1]} were collecting data, they split the data into view-dependent and view-independent categories according to whether the description was constrained to the speaker's view. They have provided additional view guidance, e.g., “Facing the bed” in the example of Fig. REF , to those view-dependent data. In practice, the view constraint is usually implicit, and we cannot always rely on speakers to provide augmented descriptions to solve the problem. Especially, the robot or the intelligent agent should be able to ground to the target object no matter how its view is different from the speaker's. Thus, developing a robust approach to view changes becomes imperative in 3D visual grounding.
